Transit BI needs dashboards that stay fast during rush reporting on hourly boardings. Which Unity Catalog object should they use for pre-aggregated metrics?

Transit BI needing dashboards that stay fast during rush reporting on hourly boardings should use a materialized view that pre-aggregates hourly boardings for rush reporting. Materialized views store precomputed results so dashboards query rolled-up metrics instead of rescanning raw fare-card or boarding facts on every rush-hour refresh—improving latency and reducing warehouse load for municipal operations centers.

An external table that pre-aggregates hourly boardings for rush reporting cannot actually persist a precomputed rollup; it only maps to underlying files and still scans raw rows on every dashboard refresh, so it is slow and brittle for rush reporting. A standard view that pre-aggregates hourly boardings for rush reporting remains a logical definition that recomputes the aggregation on every read, so it cannot deliver the persisted, pre-aggregated performance a materialized view gives rush dashboards. A Unity Catalog volume that pre-aggregates hourly boardings for rush reporting is still just file storage with no compute engine; dashboards must scan and aggregate those files at query time instead of reading pre-aggregated metrics.

Exam caveat: materialized views vs standard views—standard views recompute on read; MVs persist precomputed results for performance. Do not be misled by pre-aggregates wording in the distractors; only the materialized view persists precomputed results for dashboards. Volumes remain correct for files but not as a substitute for aggregated BI datasets.

Operational check: define a UC materialized view aggregating hourly boardings, refresh per transit SLA, point rush dashboards at the MV via SQL warehouse, and compare query times against raw fact scans during peak reporting.